Issue No. 06 - November/December (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.101
Murat Can Cobanoglu , Sabanci University, Istanbul
Yucel Saygin , Sabanci University, Istanbul
Ugur Sezerman , Sabanci University, Istanbul
The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this work, we propose a method for Class A subfamily classification using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites. The motifs that best characterize a subfamily are selected by the Distinguishing Power Evaluation (DPE) technique we propose. The experiments performed on GPCR sequence databases show that our method outperforms state-of-the-art classification techniques for GPCR Class A subfamily prediction. An important contribution of our work is to discover key receptor-ligand interaction sites which is very important for drug design.
Sequence analysis, GPCR classification, data mining, motif selection.
Y. Saygin, U. Sezerman and M. C. Cobanoglu, "Classification of GPCRs Using Family Specific Motifs," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1495-1508, 2010.